由於自走車的SLAM建置地圖的普及化，本研究構想是想讓無人機也能達到該目標，讓機體搭配LiDAR能感測環境且建置地圖，該技術亦是一項新的趨勢。傳統上，若以無人機搭配LiDAR輔助，是用其雷射測距功能達到無人機的高度或側向的避障，但在本研究中，將以LiDAR建置SLAM平面地圖，所採用的演算方法為Hector SLAM，其運算核心是雷射掃描匹配的方法，實驗中搭載於四旋翼機進行室內地圖繪製。由於室內飛行並沒有GPS的定位輔助，故飛行的狀況會很不穩定，機身的俯仰、側傾、加速、減速都會造成LiDAR掃描面有重複疊圖的狀況或是測距的長度不準確，故本論文提出搭配使用卡爾曼濾波器的預測及估計應用於掃描匹配，以建出SLAM地圖。最後藉由掃描匹配的誤差分析，探討實驗的結果。

Due to the extensive use of SLAM maps for autonomous cars, this idea of study presents 2D SLAM mapping construction on a quadcopter in the indoor environments. Traditionally, the application of Lidar for drones are often used for lateral and height collision avoidance. However, in this research, the implementation of LiDAR will be used for 2D map construction by quadcopter. The proposed method of SLAM is Hector SLAM algorithm, which is used by laser scanning matching method without any information of odometry. For large-scale indoor laser scan mapping, a challenging problem is that any vibration or motion resulting from drones, such as pitch, roll, acceleration and deceleration, will cause repeated overlapping maps or inaccurate distance and orientation measurement on the LiDAR scanning surface. Thus, this thesis proposes that the prediction and estimation using Kalman filter is applied to PIXHAWK IMU and assist LiDAR scan matching to build a SLAM map. Last, the results of the experiment will be discussed through the error analysis of scan matching.

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